When a person is having an issue with a vehicle and needs assistance or service, time is of the essence. Even small steps taken to reduce wait time for a user and get the vehicle issue addressed more quickly can make a difference. Conventional roadside assistance systems rely on a roadside assistance associate who may be bound by a script including questions that must be asked in a particular order. The information may then be input into the system in the required order. However, this process can be inefficient and time consuming for the user.
In addition, conventional systems rely on roadside assistance associates to identify and/or dispatch appropriate service providers to address the needs of the user. This is prone to error which can cause further delays.
Accordingly, it would be advantageous to provide a system in which necessary information is captured in an efficient way and machine learning is used to evaluate roadside assistance requests and generate roadside assistance instructions.